Mike Palmer of Sigma Computing, chief executive officer, discusses enterprise data democratization and artificial intelligence with hosts Gemma Allen and John Furrier on theCUBE and NYSE Wired: Mixture of Experts, with research support from theCUBE Research.
Palmer explains Sigma's approach to making analytics accessible to spreadsheet users, integrating with platforms such as Snowflake and Databricks and advancing AI agents and application building. They highlight the importance of curated semantic layers for reliable data and the need for governance and IT controls when business users create applications or agents.
The discussion also addresses consolidation pressures in the software as a service market and service opportunities arising from the so-called SaaSpocalypse. Viewers gain practical insights on data democratization, enterprise analytics, collaboration, semantic layers, governance and AI-driven analytics.
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Mike Palmer, Sigma Computing
Mike Palmer of Sigma Computing, chief executive officer, discusses enterprise data democratization and artificial intelligence with hosts Gemma Allen and John Furrier on theCUBE and NYSE Wired: Mixture of Experts, with research support from theCUBE Research.
Palmer explains Sigma's approach to making analytics accessible to spreadsheet users, integrating with platforms such as Snowflake and Databricks and advancing AI agents and application building. They highlight the importance of curated semantic layers for reliable data and the need for governance and IT controls when business users create applications or agents.
The discussion also addresses consolidation pressures in the software as a service market and service opportunities arising from the so-called SaaSpocalypse. Viewers gain practical insights on data democratization, enterprise analytics, collaboration, semantic layers, governance and AI-driven analytics.
>> ...Palo Alto studio connecting Silicon Valley and Wall Street. I'm John Furrier to be here with Dave Vellante, my co-host.
Gemma Allen
>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired's Mixture of Experts. And joining me now for a conversation on the world of data and AI is Mike Palmer, CEO of Sigma Computing. Welcome, Mike.
Mike Palmer
>> Thank you for having me here.
Gemma Allen
>> So for those not familiar, Sigma Computing, boil it down. What it is exactly that you guys do?
Mike Palmer
>> So customers, over the last five or six years, have been moving enormous amounts of data into platforms like Databricks and Snowflake, and the volume of data is enormous, and the technical skills to access the data were significant. And what Sigma did was find a way to give the average person that knew how to do things like run a spreadsheet, the ability to do massive amounts of data analytics without having to have that expert involved. Of course, since then with AI, that's evolved into building agents, building full workflows and applications, and so we now service some of the biggest companies in the world. We were talking about this earlier before the interview, including the New York Stock Exchange, to your favorite banks, to your favorite retailers.
Gemma Allen
>> Wow. So use maybe ICE and the NYSE as an example. Talk us through one of your users here, how they use it, in what capacity.
Mike Palmer
>> Banks have enormous demand for data, especially granular data, because in the end, financial services companies are arbitrage-based companies, right?
Gemma Allen
>> Right.
Mike Palmer
>> If they can find something in the data, they have an advantage. So at a bank, that is everything from, what I call, the back office. If I'm working in compliance, I want to find market manipulation. So we have customers like DTCC and, of course, ICE doing that work with us. If I'm in the front office at a JPMorgan, I'm doing the global banking, wealth management. I'm in corporate finance. I'm trying to build models. I'm trying to use agents to find information about, let's say, my portfolio that I wasn't able to find it myself or didn't want to spend the extra or couldn't spend the extra effort to do. So the use cases abound. It's in every department across every vertical.
Gemma Allen
>> Wow. And you joined in 2020, or you became CEO in 2020.
Mike Palmer
>> That's right.
Gemma Allen
>> A lot has happened in six years in the space of tech and data. You've mentioned Snowflake, Databricks. We hear about data lakes, data warehouses. Data is the fuel for AI, right? AI is nothing without the right data behind it.
Mike Palmer
>> That's right.
Gemma Allen
>> Talk to us a little bit about what you're seeing in the market, especially in this moment we're in right now. How are the demands shifting and what sorts of nuances are you seeing too? Because data has always been a challenge for a lot of large enterprises, especially getting clean, structured data.
Mike Palmer
>> For sure. And I think that the databases are doing a great job of emerging from the let's get data in the warehouse to a let's get really curated data there. And so some of the investments that those companies are making include the semantic views for Snowflake or Unity Catalog for Databricks. And this is where it's not just my data, but I've actually put some effort into modeling that data and making sure it reflects the way that we think about our businesses. The problem Sigma's then solving for users on top of that is great. I've got model data, but I want to run my department's workflows there. I want to add my own value, my forecasting value. I want to be able to collaborate with colleagues on building analytics. I want to build completely novel types of workflows. And so where the business is really going is going away from, or better said, building on, let's do a better job with data, which we've wanted to do for decades. Data democratization is one of the oldest terms in technology, to I really just wanted to act on that data once I discovered that nuance that you referred to. That means rebuilding departmental software. It means automating things that I used to download into spreadsheets and then send over email. And now it means telling agents, who can do their own reasoning, to do my job for me in some ways. So we're seeing users build agents that work in the background on their behalf all working on this curated data set so it's accurate all within the context of a user who knows their department really well, but didn't necessarily have technical skills to get access to that data before Sigma came along.
Gemma Allen
>> So when we think about data, we think about discoverability, structure. We also think about visualization, right? I know you guys compete with Power BI. I was a one-time Power BI user, and I struggled because sometimes how you generate these queries, I worked in the days of T-SQL. That was pretty painful. You may or may not remember. Talk about what AI is doing in terms of it creating an accessibility layer for data. For folks who would have had to request queries, wait three days, what sorts of rhythms and cadences are you seeing across enterprises now?
Mike Palmer
>> I think the first one was the one you mentioned, which is why do I have to have a technical skill to apply my business knowledge skill? If I work in marketing or inventory, why do I have to know SQL to do my marketing job? And AI has done a great job of layering yet another access method. So that could be a coding agent. It could be just natural language in general where I can use what I basically got access to when I turned two. I can speak the language. AI is doing a great job of interpreting that language into technical back speak, like SQL, and giving me answers without me having to ever have learned the system or so to speak. Where that is now going is full-fledged applications being built with language. So we've emerged from can I get access to my data to can I build a complex application without a developer? And that's going to save companies enormous amounts of money. It's going to collapse silos of applications that exist today. It's going to give us the ability to write apps that we never even thought of before. And this is just the next layer, if you will, or the next opportunity for us to be more productive, to scale AI, to be reaping some of those benefits we've been hearing about when we're talking about AI having a big economic impact on enterprise.
Gemma Allen
>> And we think about those connective tissues at enterprise, one app talking to another app, the HR department talking to the financial forecasting department, whatever it might be. There has been silos for a reason, right? There has been protective layers in place because governance and compliance and the technology underneath had to be sound. We're now in a place where suddenly it's like, wow, the race is on and everything's just happening. It's like a form of wizardry. We're like wizards. What are you seeing from the perspective of risk though and some level of a control plane around how we think about the data layer across enterprise?
Mike Palmer
>> And this is exactly where Sigma comes in because when you have a lot of freedom on the frontend, you want to have a lot of control on the backend. I metaphorically talk about letting the kids play in the trees, but you want to put some sort of safety net under them. And if our end users are now going to do the job that IT used to do, which is, I'm going to build an application. I'm going to share it with my colleagues. I'm going to start using it every day, well, this is something IT used to do for us, and they used to make sure about things like when we changed it, it didn't break, that the access that we had to data was the right access to data, that we could report on it to our auditors. But if our end users are now building these things and they're not IT experts, they'll never consider all of these important backend capabilities. What Sigma is bringing to enterprise is the ability to vibe code that frontend, whether it's your analytics, whether it's a new application, whether it's creating an agent, but providing the IT team the knowledge that when they connect to that data, they're only getting access to the data that they individually were allowed. If they build an application, we can report to our auditors on who used it and what data it touched. If they're changing it, that they go through a, what we call, an SDLC workflow so it gets tested before it gets rolled out into the day-to-day work environment so we don't have outages. All of those things that IT still needs, even in a world of vibe coding, we're providing to them while giving that frontend vibe code experience to the business user.
Gemma Allen
>> And that first step, that fundamental shift to ensure that the data is sound, it's clean, it's accessible, it's structured, what are you seeing in that space? What other partners or technologies are you seeing that's really suddenly having this almost magical impact on data sets that we know across enterprise have existed for even decades, right? The discoverability is huge.
Mike Palmer
>> Discovering data, and more importantly, discovering data that reflects what the enterprise wants it to reflect. If I ask a question about revenue, I shouldn't be finding the 20 tables that might have something to do with revenue, but might not actually be the right revenue numbers. I think our warehouse partners are doing an exceptional job of building semantics that allow our data engineering teams to curate that data and then creating access methods for all of us to consume it. And I think that path is pretty well trodden at this point. And the role for the rest of us then is what can we build on it because we think about that layer as foundational. But when it is really valuable is when that marketer takes it and says, well, yes, I now know the revenue data, but I combine revenue data with pipeline data. I combine that with data on my paid ad spend, and I start to pull all that together so that I can start to think about how much should I spend to generate net new revenue in the future. So that extra knowledge that I'm bringing about my job that I'm putting into this system now is uniquely capable inside of Sigma. And we give them some additional capabilities not just to model data on top of that semantic layer, but critically we give them the ability to write to the Snowflake and Databricks systems, which is something that is unique in the industry. And we find that quite ironic because the knowledge that is typically not captured is the knowledge from their own employees, which we're now making available.
Gemma Allen
>> And when you think about the ability to help them do that, that is a services play too, right? And we've seen a lot of services companies pop up over the last year or two offering all sorts of ways in which they can help companies optimize AI, optimize visualization. How are you thinking about that part of your own business?
Mike Palmer
>> Well, the big move in the market now, as everyone knows from the multiples in software, is this idea of SaaSpocalypse. And I think while there are a lot of different opinions about software and the future of software, it's pretty clear it's going to go through some sort of transformation. And it's likely that that will include reversing the proliferation that we've seen in the SaaS world where every use case in every department became a standalone company. And as our enterprise customers are thinking about what's the right next architecture, it's most certainly going to be how do I do many, many workflows on consolidated platforms? And this is where service providers do an exceptional job translating business requirements into novel workflows or into rebuilt workflows into platforms like Sigma helping customers retire many third-party software products that they otherwise had to manage and maintain.
Gemma Allen
>> When we think about this SaaSpocalypse, part of the premise of that argument is that there will be one sole harness provider, right? I mean, there's all sorts of speculation around whether that's going to be Anthropic or who that could possibly be, but there is obviously a level of naivety in thinking that any one company, like any Fortune 50 company is going to really rely on one entity for everything, right? But at the same time, there is definitely a little bit of parts of everyone's lunch are being eaten in certain ways.
Mike Palmer
>> That's right.
Gemma Allen
>> So when you think about the moat of your own company and what you provide, how do you think about that from the perspective of competing with a harness layer, for example, that we can do everything all at once view that may or may not be accurate?
Mike Palmer
>> Well, the model of providers are really interesting in a few different ways, but there's no doubt that the new toy is AI. And at times when you have something completely new, it looks like a hammer and everything looks like a nail once you have that hammer. I think what customers find is there are some really important areas that AI won't cover for you. Use the word harness. In fact, that's what they're not. If I build an application in an AI LLM, I really can't run it there. I have to find somewhere else to do that. And that's critical for enterprise because we don't have individual workflows. Enterprise is a team sport. So we have to run this for all of our colleagues that can scale to tens of thousands of people that need to work each and every day. We need to evolve it. These are things that the model providers aren't even trying to do for you. So this is where providers like Sigma step in. There are outstanding access methods. Using coding agents for the even average person has been a revolution in the ability to build really novel things. To run them and to evolve them is a challenge that we're trying to step in and help those customers manage over time.
Gemma Allen
>> And Mike, financials, you guys just had a raise-
Mike Palmer
>> That's right....
Gemma Allen
>> I think a month ago, 80 million?
Mike Palmer
>> We raised $80 million just to shore up the balance sheet at $3 billion.
Gemma Allen
>> Wow, congrats.
Mike Palmer
>> We're happy to have Princeville that led that round. We have amazing investors like Sutter Hill Ventures, Altimeter, Spark, Avenir, XN, D1. That are all very well known in the industry and all followed in that. But we're just crossing 250 million in ARR, growing 80% year-over-year at scale.
Gemma Allen
>> Wow.
Mike Palmer
>> So we have a lot of momentum behind us. We think we're only getting started. As we talked about before starting this interview, we hope to be, in the next couple of years, maybe partaking in some of the festivities here as we get to that scale.
Gemma Allen
>> Well, we certainly hope so too. Last question, $80 million, where's that money going to be spent? Are you investing in R&D, investing more into the tech? What is the key focus for the next 12 months or before we hopefully see you on that podium?
Mike Palmer
>> You know what's interesting is we've gotten to this point where being a profitable company, the point of that is more balance sheet strength that can be deployed when we think that it is necessary. We have a super efficient business at this point and revenue has taken over the cost lines are, so to speak, and scaling much faster. So it's really just the ability to make decisions when we want to make them and take advantage of opportunities when we see them. And whether we do that again or not, we'll see going forward, but we're privileged in the ability to be in control of our own destiny.
Gemma Allen
>> Well, Mike Palmer, that's a great place to be. Thank you so much for joining us on theCUBE.
Mike Palmer
>> Thank you for having me.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired's Mixture of Experts. Thanks for watching.
>> ...Palo Alto studio connecting Silicon Valley and Wall Street. I'm John Furrier to be here with Dave Vellante, my co-host.
Gemma Allen
>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen with NYSE Wired's Mixture of Experts. And joining me now for a conversation on the world of data and AI is Mike Palmer, CEO of Sigma Computing. Welcome, Mike.
Mike Palmer
>> Thank you for having me here.
Gemma Allen
>> So for those not familiar, Sigma Computing, boil it down. What it is exactly that you guys do?
Mike Palmer
>> So customers, over the last five or six years, have been moving enormous amounts of data into platforms like Databricks and Snowflake, and the volume of data is enormous, and the technical skills to access the data were significant. And what Sigma did was find a way to give the average person that knew how to do things like run a spreadsheet, the ability to do massive amounts of data analytics without having to have that expert involved. Of course, since then with AI, that's evolved into building agents, building full workflows and applications, and so we now service some of the biggest companies in the world. We were talking about this earlier before the interview, including the New York Stock Exchange, to your favorite banks, to your favorite retailers.
Gemma Allen
>> Wow. So use maybe ICE and the NYSE as an example. Talk us through one of your users here, how they use it, in what capacity.
Mike Palmer
>> Banks have enormous demand for data, especially granular data, because in the end, financial services companies are arbitrage-based companies, right?
Gemma Allen
>> Right.
Mike Palmer
>> If they can find something in the data, they have an advantage. So at a bank, that is everything from, what I call, the back office. If I'm working in compliance, I want to find market manipulation. So we have customers like DTCC and, of course, ICE doing that work with us. If I'm in the front office at a JPMorgan, I'm doing the global banking, wealth management. I'm in corporate finance. I'm trying to build models. I'm trying to use agents to find information about, let's say, my portfolio that I wasn't able to find it myself or didn't want to spend the extra or couldn't spend the extra effort to do. So the use cases abound. It's in every department across every vertical.
Gemma Allen
>> Wow. And you joined in 2020, or you became CEO in 2020.
Mike Palmer
>> That's right.
Gemma Allen
>> A lot has happened in six years in the space of tech and data. You've mentioned Snowflake, Databricks. We hear about data lakes, data warehouses. Data is the fuel for AI, right? AI is nothing without the right data behind it.
Mike Palmer
>> That's right.
Gemma Allen
>> Talk to us a little bit about what you're seeing in the market, especially in this moment we're in right now. How are the demands shifting and what sorts of nuances are you seeing too? Because data has always been a challenge for a lot of large enterprises, especially getting clean, structured data.
Mike Palmer
>> For sure. And I think that the databases are doing a great job of emerging from the let's get data in the warehouse to a let's get really curated data there. And so some of the investments that those companies are making include the semantic views for Snowflake or Unity Catalog for Databricks. And this is where it's not just my data, but I've actually put some effort into modeling that data and making sure it reflects the way that we think about our businesses. The problem Sigma's then solving for users on top of that is great. I've got model data, but I want to run my department's workflows there. I want to add my own value, my forecasting value. I want to be able to collaborate with colleagues on building analytics. I want to build completely novel types of workflows. And so where the business is really going is going away from, or better said, building on, let's do a better job with data, which we've wanted to do for decades. Data democratization is one of the oldest terms in technology, to I really just wanted to act on that data once I discovered that nuance that you referred to. That means rebuilding departmental software. It means automating things that I used to download into spreadsheets and then send over email. And now it means telling agents, who can do their own reasoning, to do my job for me in some ways. So we're seeing users build agents that work in the background on their behalf all working on this curated data set so it's accurate all within the context of a user who knows their department really well, but didn't necessarily have technical skills to get access to that data before Sigma came along.
Gemma Allen
>> So when we think about data, we think about discoverability, structure. We also think about visualization, right? I know you guys compete with Power BI. I was a one-time Power BI user, and I struggled because sometimes how you generate these queries, I worked in the days of T-SQL. That was pretty painful. You may or may not remember. Talk about what AI is doing in terms of it creating an accessibility layer for data. For folks who would have had to request queries, wait three days, what sorts of rhythms and cadences are you seeing across enterprises now?
Mike Palmer
>> I think the first one was the one you mentioned, which is why do I have to have a technical skill to apply my business knowledge skill? If I work in marketing or inventory, why do I have to know SQL to do my marketing job? And AI has done a great job of layering yet another access method. So that could be a coding agent. It could be just natural language in general where I can use what I basically got access to when I turned two. I can speak the language. AI is doing a great job of interpreting that language into technical back speak, like SQL, and giving me answers without me having to ever have learned the system or so to speak. Where that is now going is full-fledged applications being built with language. So we've emerged from can I get access to my data to can I build a complex application without a developer? And that's going to save companies enormous amounts of money. It's going to collapse silos of applications that exist today. It's going to give us the ability to write apps that we never even thought of before. And this is just the next layer, if you will, or the next opportunity for us to be more productive, to scale AI, to be reaping some of those benefits we've been hearing about when we're talking about AI having a big economic impact on enterprise.
Gemma Allen
>> And we think about those connective tissues at enterprise, one app talking to another app, the HR department talking to the financial forecasting department, whatever it might be. There has been silos for a reason, right? There has been protective layers in place because governance and compliance and the technology underneath had to be sound. We're now in a place where suddenly it's like, wow, the race is on and everything's just happening. It's like a form of wizardry. We're like wizards. What are you seeing from the perspective of risk though and some level of a control plane around how we think about the data layer across enterprise?
Mike Palmer
>> And this is exactly where Sigma comes in because when you have a lot of freedom on the frontend, you want to have a lot of control on the backend. I metaphorically talk about letting the kids play in the trees, but you want to put some sort of safety net under them. And if our end users are now going to do the job that IT used to do, which is, I'm going to build an application. I'm going to share it with my colleagues. I'm going to start using it every day, well, this is something IT used to do for us, and they used to make sure about things like when we changed it, it didn't break, that the access that we had to data was the right access to data, that we could report on it to our auditors. But if our end users are now building these things and they're not IT experts, they'll never consider all of these important backend capabilities. What Sigma is bringing to enterprise is the ability to vibe code that frontend, whether it's your analytics, whether it's a new application, whether it's creating an agent, but providing the IT team the knowledge that when they connect to that data, they're only getting access to the data that they individually were allowed. If they build an application, we can report to our auditors on who used it and what data it touched. If they're changing it, that they go through a, what we call, an SDLC workflow so it gets tested before it gets rolled out into the day-to-day work environment so we don't have outages. All of those things that IT still needs, even in a world of vibe coding, we're providing to them while giving that frontend vibe code experience to the business user.
Gemma Allen
>> And that first step, that fundamental shift to ensure that the data is sound, it's clean, it's accessible, it's structured, what are you seeing in that space? What other partners or technologies are you seeing that's really suddenly having this almost magical impact on data sets that we know across enterprise have existed for even decades, right? The discoverability is huge.
Mike Palmer
>> Discovering data, and more importantly, discovering data that reflects what the enterprise wants it to reflect. If I ask a question about revenue, I shouldn't be finding the 20 tables that might have something to do with revenue, but might not actually be the right revenue numbers. I think our warehouse partners are doing an exceptional job of building semantics that allow our data engineering teams to curate that data and then creating access methods for all of us to consume it. And I think that path is pretty well trodden at this point. And the role for the rest of us then is what can we build on it because we think about that layer as foundational. But when it is really valuable is when that marketer takes it and says, well, yes, I now know the revenue data, but I combine revenue data with pipeline data. I combine that with data on my paid ad spend, and I start to pull all that together so that I can start to think about how much should I spend to generate net new revenue in the future. So that extra knowledge that I'm bringing about my job that I'm putting into this system now is uniquely capable inside of Sigma. And we give them some additional capabilities not just to model data on top of that semantic layer, but critically we give them the ability to write to the Snowflake and Databricks systems, which is something that is unique in the industry. And we find that quite ironic because the knowledge that is typically not captured is the knowledge from their own employees, which we're now making available.
Gemma Allen
>> And when you think about the ability to help them do that, that is a services play too, right? And we've seen a lot of services companies pop up over the last year or two offering all sorts of ways in which they can help companies optimize AI, optimize visualization. How are you thinking about that part of your own business?
Mike Palmer
>> Well, the big move in the market now, as everyone knows from the multiples in software, is this idea of SaaSpocalypse. And I think while there are a lot of different opinions about software and the future of software, it's pretty clear it's going to go through some sort of transformation. And it's likely that that will include reversing the proliferation that we've seen in the SaaS world where every use case in every department became a standalone company. And as our enterprise customers are thinking about what's the right next architecture, it's most certainly going to be how do I do many, many workflows on consolidated platforms? And this is where service providers do an exceptional job translating business requirements into novel workflows or into rebuilt workflows into platforms like Sigma helping customers retire many third-party software products that they otherwise had to manage and maintain.
Gemma Allen
>> When we think about this SaaSpocalypse, part of the premise of that argument is that there will be one sole harness provider, right? I mean, there's all sorts of speculation around whether that's going to be Anthropic or who that could possibly be, but there is obviously a level of naivety in thinking that any one company, like any Fortune 50 company is going to really rely on one entity for everything, right? But at the same time, there is definitely a little bit of parts of everyone's lunch are being eaten in certain ways.
Mike Palmer
>> That's right.
Gemma Allen
>> So when you think about the moat of your own company and what you provide, how do you think about that from the perspective of competing with a harness layer, for example, that we can do everything all at once view that may or may not be accurate?
Mike Palmer
>> Well, the model of providers are really interesting in a few different ways, but there's no doubt that the new toy is AI. And at times when you have something completely new, it looks like a hammer and everything looks like a nail once you have that hammer. I think what customers find is there are some really important areas that AI won't cover for you. Use the word harness. In fact, that's what they're not. If I build an application in an AI LLM, I really can't run it there. I have to find somewhere else to do that. And that's critical for enterprise because we don't have individual workflows. Enterprise is a team sport. So we have to run this for all of our colleagues that can scale to tens of thousands of people that need to work each and every day. We need to evolve it. These are things that the model providers aren't even trying to do for you. So this is where providers like Sigma step in. There are outstanding access methods. Using coding agents for the even average person has been a revolution in the ability to build really novel things. To run them and to evolve them is a challenge that we're trying to step in and help those customers manage over time.
Gemma Allen
>> And Mike, financials, you guys just had a raise-
Mike Palmer
>> That's right....
Gemma Allen
>> I think a month ago, 80 million?
Mike Palmer
>> We raised $80 million just to shore up the balance sheet at $3 billion.
Gemma Allen
>> Wow, congrats.
Mike Palmer
>> We're happy to have Princeville that led that round. We have amazing investors like Sutter Hill Ventures, Altimeter, Spark, Avenir, XN, D1. That are all very well known in the industry and all followed in that. But we're just crossing 250 million in ARR, growing 80% year-over-year at scale.
Gemma Allen
>> Wow.
Mike Palmer
>> So we have a lot of momentum behind us. We think we're only getting started. As we talked about before starting this interview, we hope to be, in the next couple of years, maybe partaking in some of the festivities here as we get to that scale.
Gemma Allen
>> Well, we certainly hope so too. Last question, $80 million, where's that money going to be spent? Are you investing in R&D, investing more into the tech? What is the key focus for the next 12 months or before we hopefully see you on that podium?
Mike Palmer
>> You know what's interesting is we've gotten to this point where being a profitable company, the point of that is more balance sheet strength that can be deployed when we think that it is necessary. We have a super efficient business at this point and revenue has taken over the cost lines are, so to speak, and scaling much faster. So it's really just the ability to make decisions when we want to make them and take advantage of opportunities when we see them. And whether we do that again or not, we'll see going forward, but we're privileged in the ability to be in control of our own destiny.
Gemma Allen
>> Well, Mike Palmer, that's a great place to be. Thank you so much for joining us on theCUBE.
Mike Palmer
>> Thank you for having me.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired's Mixture of Experts. Thanks for watching.
John Furrier
Gemma Allen